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Pertemuan 8 Representing Knowledge Using Rules

Pertemuan 8 Representing Knowledge Using Rules . Matakuliah : T0264/Inteligensia Semu Tahun : 2005 Versi : 1. Learning Outcomes. Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : << TIK-99 >> << TIK-99>>. Outline Materi. Materi 1 Materi 2 Materi 3 Materi 4 Materi 5.

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Pertemuan 8 Representing Knowledge Using Rules

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  1. Pertemuan 8Representing Knowledge Using Rules Matakuliah : T0264/Inteligensia Semu Tahun : 2005 Versi : 1

  2. Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : • << TIK-99 >> • << TIK-99>>

  3. Outline Materi • Materi 1 • Materi 2 • Materi 3 • Materi 4 • Materi 5

  4. 6.1 Procedural vs Declarative Knowledge Consider the knowledge base : man(Marcus) man(Caesar) person(Cleopatra) x : man(x)  person(x) Supose we want to answer the question y : person(y)

  5. 6.1 Procedural vs Declarative Knowledge We could answer with any one of : y = Marcus y = Caesar y = Cleopatra Now consider an alternative KB : man(Marcus) man(Caesar) x : man(x)  person(x) person(Cleopatra)

  6. 6.2 Logic Programming PROLOG A PROLOG program is composed of a set of Horn clauses. A Horn clause is a clause that has at most one positive literal. Examples : p p q r  s r  s

  7. 6.2 Logic Programming A Declarative and a Procedural Representation A Representation in Logic x : pet(x) small(x) apartmentpet(x) x : cat(x) dog(x) pet(x) x : poodle(x)  dog(x) small(x) poodle(fluffy)

  8. 6.2 Logic Programming A Representation in PROLOG apartmentpet(x) : - pet(x), small(x). pet(x) : - cat(x). pet(x) : - dog(x). dog(x) : - poodle(x). small(x) : - poodle(x). poodle(fluffy).

  9. 6.2 Logic Programming Answering Question in PROLOG ?- apartmentpet(x). ?- cat(fluffy). ?- cat(mittens).

  10. 6.3 Forward vs Backward Reasoning • Number of start and goal states. • Branching factor in each direction. • Need to justify reasoning. • Triggers for problem solving.

  11. 6.3 Forward vs Backward Reasoning Sample or the Rules for Solving the 8- Puzzle Assume the areas of the tray are numbered:

  12. 6.3 Forward vs Backward Reasoning Square 1 empty and Square 2 contains tile n Square 2 empty and Square 1 contains tile n Square 1 empty and Square 4 contains tile n Square 4 empty and Square 1 contains tile n Square 2 empty and Square 1 contains tile n Square 1 empty and Square 2 contains tile n

  13. 6.3 Forward vs Backward Reasoning An Example :

  14. 6.3 Forward vs Backward Reasoning A Bad Use of Heuristic Bidirectional Search

  15. 6.4 Matching Representation Affects Matching

  16. 6.4 Matching White pawn at Square(file e, rank 2) AND Square(file e, rank 3)  move pawn from is empty Square(file e, rank 2) AND to Square(file e, rank 4) Square(file e, rank 4) is empty

  17. 6.4 Matching Many-to-Many Matching • The temporal nature of data. • Structural similarity in rules. mammal(x)  feline(x)  jaguar(x) carnivorous(x)  has-spots(x) mammal(x)  feline(x)  tiger(x) carnivorous(x)  has-stripes(x)

  18. 6.4 Matching • Persistance of variable binding consistency. son(x,y)   grandparent(x,z) son(y,z)

  19. 6.4 Matching A Bit of Dialogue with ELIZA

  20. 6.4 Matching

  21. 6.4 Matching

  22. 6.4 Matching Some ELIZA - like rules (X me Y)  (X you Y) (I remember Z)  (Why do remember X just now ?) (My {family-member} is Y)  (Who else in your family is Y) (X {family-member} Y) (Tell me more about your family)

  23. 6.4 Matching Conflict Resolution • Preferences based on rules • Rule order • Prefer special cases over more general ones • Preferences based on objects • Prefer some objects to others • location in STM • Preferences based on states

  24. 6.5 Control Knowledge Syntax for a Control Rule Under conditions A and B, Rules that do {not} mention X { at all, in their left-hand side, in their right-hand side}

  25. 6.5 Control Knowledge will { definitely be useless, probably be useless ... probably be especially useful definitely be especially useful}

  26. << CLOSING>>

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